Claude’s automated researchers close 26% to 96% of safety gap across alignment failures

Anthropic just published results that read like a plot twist in the AI safety debate: the AI is now better at making AI safe than the humans are.

The company’s study, titled “Automated researchers can reliably mitigate alignment failures,” shows that Claude-powered automated alignment researchers (AARs) closed between 26% and 96% of the “safety gap” across 10 distinct categories of alignment failures. On deceptive behaviors specifically, Claude’s AARs scored 82% to 85%, outperforming 28 experienced human safety researchers by roughly 20 percentage points.

What the study actually tested

The AARs followed a structured workflow. They conducted literature searches, proposed mitigation methods, trained models for roughly 30 minutes on a single H200 GPU, and then ran rigorous benchmark evaluations. Each failure type saw upwards of 150 evaluation attempts, a volume of systematic experimentation that would be brutal for human researchers to match manually.

The human comparison group wasn’t a bunch of interns. Twenty-eight seasoned safety researchers were given up to eight hours per task.

Generalization is the real headline

Anthropic’s results suggest Claude’s methods generalized effectively to withheld datasets that weren’t part of the original evaluation. They also performed well on the open-source Petri auditing tool, which tests models against complex adversarial scenarios.

Earlier work from Anthropic, published in April 2026, already hinted at this trajectory. In weak-to-strong supervision tasks, where a less capable model tries to supervise a more capable one, AARs recovered 97% of the performance gap. Human efforts on the same task recovered just 23%.

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